As the cost of GPUs continues to rise, GPU-sharing solutions have become increasingly important for improving efficiency and maximizing resource utilization. At the same time, large-scale operational deployments of such solutions remain relatively less explored, especially in heterogeneous production environments where workload dynamics and orchestration complexity introduce new practical considerations. In this paper, we introduce eGPU, an elastic, efficient, and scalable GPU-sharing framework tailored for production-scale concurrent machine learning (ML) training and inference. eGPU enables fine-grained, runtime-adjustable sharing of GPUs across multiple jobs, while preserving high resource utilization and fault isolation. To address communication bottlenecks, eGPU supports native NVLink/NCCL-based communication between shared GPU instances, capabilities that are limited or unavailable in many existing designs. Built with production deployment in mind, eGPU integrates with Kubernetes (K8s) to support large-scale orchestration. It has been deployed and running stably in production clusters with over$\text{1 0, 0 0 0 ~ G P U s}$for five years. Our evaluation results show that eGPU achieves elastic and precise control over instance sizes, improves job efficiency by 21 % to 31% than SOTA sharing solutions, saves the number of GPUs required by up to$8 \times$, and improves cluster GPU utilization by more than$\mathrm{3} \times$.

Human-object interaction (HOI) detection is a meaningful research topic on human activity understanding. Recent works have made significant progress by focusing on efficient triplet matching and leveraging image-wide features based on encoder-decoder architecture. However, the ability to gather relevant contextual information about human is limited and different sub-tasks in HOI detection are not differentiated by specific decoupling in previous methods. To this end, we propose a new transformer-based method for HOI detection, namely, Mask-Guided Transformer (MGT). Our model, which is composed of five parallel decoders with a shared encoder, not only emphasizes interactive regions by applying body features, but also disentangles the prediction of instance and interaction. We achieve a favorable result at 63.3 mAP on the well-known HOI detection dataset V-COCO.